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| 1 | Simulation of nucleate boiling under ANSYS-FLUENT code by using RPI model coupling with artificial neural networks显示文摘The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear reactor.Indeed,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal hydraulics.Contrary to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial interpolations.For that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT.The technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated vapor.Temperature is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this area.In fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow. | Brahim Mohamedi Salah Hanini Abdelrahmane Ararem Nacim Mellel | 2015 | Nuclear Science and Techniques2015，26，4： | 5 |
| 2 | Focal Laser Ablation of Prostate Cancer: Definition, Needs, and Future显示文摘 | Pierre Colin Serge Mordon Pierre Nevoux Mohammed Feras Marqa Adil Ouzzane Philippe Puech Gregory Bozzini Bertrand Leroux Arnauld Villers Nacim Betrouni Eric Barret | 2012 | Advances in Urology2012，，： | 1 |
| 3 | A new ehymotrypsin-like serine protease involved in dietary protein digestion in a primitive animal, Scopio maurus: purification and biochemical characterization 显示文摘 | Hanen Louati Nacim Zouari Nabil Miled | 2011 | Lipids Health Dis2011，10，1： | 1 |
| 4 | The role of MRI-targeted and confirmatory biopsies for cancer upstaging at selection in patients considered for active surveillance for clinically low-risk prostate cancer显示文摘 | Fran?ois Marliere Philippe Puech Ahmed Benkirane Arnauld Villers Laurent Lemaitre Xavier Leroy Nacim Betrouni Adil Ouzzane | 2014 | World Journal of Urology2014，，： | 1 |
| 5 | Combined Multiparametric MRI and Targeted Biopsies Improve Anterior Prostate Cancer Detection, Staging, and Grading显示文摘 | Adil Ouzzane Philippe Puech Laurent Lemaitre Xavier Leroy Pierre Nevoux Nacim Betrouni Georges-Pascal Haber Arnauld Villers | 2011 | Urology2011，，6： | 1 |
| 6 | Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture显示文摘The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water shortages.This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers.The proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and bean.The proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real time.This helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop failure.Our dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research centers.The experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,respectively.The new smart greenhouse automation system was also evaluated on four crops with a high turnover rate.The system has been found to be highly effective in terms of crop productivity,temperature management and water conservation. | Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood | 2023 | Computers, Materials & Continua2023，77，11： | 0 |
| 7 | Prediction of COVID-19 Transmission in the United States Using Google Search Trends显示文摘Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality. | Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 8 | Parameterization Modeling of a Gas Turbine Coverplate显示文摘 | Nacim Mesbah Sandy Seifert Bruno Chatelois Francois Gamier Hany Moustapha | 2014 | Journal of Energy and Power Engineering2014，8，8： | 0 |
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